Kihara Bioinformatics Lab Research Summary 2016
Post on 12-Apr-2017
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Daisuke KiharaProfessor of Biological Sciences, Computer Science
Bioinformatics Virtual drug screening Protein structure prediction Structure modeling for electron microscopy data
http://kiharalab.orgdkihara@purdue.eduTwitter:@kiharalab
PL-PatchSurfer2: Molecular Surface-Based Virtual Screening Method
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Compound LibraryBinding Pocket
Adenosine-binding
(Shin,Christoffer, Wang, Kihara, JCIM, 2016)
Methods Structure EF1% BEDROC
PL-PSurfer2 X-ray 12.86 0.31
TBM 11.76 0.31
Autodock Vina
X-ray 8.63 0.33
TBM 1.68 0.09
DOCK6 X-ray 11.70 0.26
TBM 2.58 0.12
http://kiharalab.org
Benchmark on the DUD dataset
• Performs better than existing methods particularly on computationally modelled target protein structures
Protein Docking
Drug Virtual Screening
Electron Microscopy
Structure Data
Ligand Binding Pocket
Comparison
http://kiharalab.org 3
Protein Function Prediction
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http://kiharalab.org/pfp_esg.php
http://kiharalab.org
Genome-Scale Detection of Moonlighting Proteins
•Proteins that have two distinct functions in a single chain
•Genome-scale prediction of moonlighting proteins
• 2.7 % of C. elegans, 7.8% of human, and 11.0 % of yeast proteins in their genomes are predicted to be moonlighting proteins
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(Khan & Kihara, Bioinformatics 2016)
http://kiharalab.org
FoldsTksfE
TksfE=)s|p(f
kf
Bjk
Bjiji /|exp(/|exp(
Protein Folding Channel
Consider information theoretic noisy channel that transmit sequence (S) information to protein structures (F, folds)
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P(fi|sj)
Boltzmann distribution:
(Magner, Szpankowski, Kihara, Scientific Reports, 2015)
http://kiharalab.org
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